Mona Jabbari
Universidade do Minho - Centro para o Território, Ambiente e Construção
FEATURES SPATIAL MODELS IN ASSESSING PEDESTRIAN NETWORK
Fernando Pereira da Fonseca
Universidade do Minho - Centro para o Território, Ambiente e Construção
Rui António Rodrigues Ramos
Universidade do Minho - Centro para o Território, Ambiente e Construção
1670
8
º CONGRESSO LUSO-BRASILEIRO PARA O PLANEAMENTO URBANO,
REGIONAL, INTEGRADO E SUSTENTÁVEL (PLURIS 2018) Cidades e Territórios - Desenvolvimento, atratividade e novos desafios
Coimbra – Portugal, 24, 25 e 26 de outubro de 2018
FEATURES SPATIAL MODELS IN ASSESSING PEDESTRIAN NETWORK
M. Jabbari, F. P. Fonseca, R. A. R. Ramos
ABSTRACT
Nowadays, urban space potential is the existed capacity, can have more efficacies to build
a sustainable urban mobility, especially walkability. Identification of the characteristics of
such spaces is an environmental challenge for the 21st century and requires new analytical
approaches and sources of data and information. Several attempts have been done to
develop Models for pedestrian network assessment, in practical urban planning. This paper
draws upon recent literature, by 3WH1 analysis based on three key questions. The
questions address the most important correlations between purposes and components of the
models introduced up to now. In essence, to answer the questions of “Where is the location
of pedestrian network?”, “What are the parameters in the pedestrian network?” and “how
to analysis such parameters?”, is the main focus of this review. In all these studies,
Geospatial Information Systems is used as an important tool for modeling, having the
advantage of a better understanding for multi-scale data.
1. INTRODUCTION
Walking is considered as one of the oldest and most important non-motorized mode of
transportation, because every journey starts and ends by walking. Pedestrian Network is
seen as one of the most important concepts for sustainable urban development and
sustainable mobility (Forsyth et al., 2009). The personal, social, economic and
environmental benefits of walking are well-documented: walking reduces traffic
congestion and pollution; it is beneficial to individuals’ health and well-being; it provides
health-economic benefits; it has impact on real estate prices and enhances the sociability
and vitality of urban spaces (Bahrainy & Khosravi, 2013; Kim, Park, & Lee, 2014). For
these reasons, Pedestrian Network has been placed at the center of a number of policies
and has been the main issue for urban designers and planners over the last decades.
The walkway facilitates pedestrian linkages to public transit, accommodating more than
200,000 business-day commuters as well as tourists and residents. However, research on
the application of Walk Score has been conducted in only five countries, with over ninety
percent being Canadian and United States based (Duncan et al., 2016; Hall & Ram, 2018).
Pedestrian network proposes a reflection on the theoretical and practical implications of the
issue, drawing on a number of research projects on the subject carried out by the authors
over more than a decade. According to finding researches in the Scopus and Web of
Science, 14 papers noticed to the pedestrian network directly that they worked in three
fields (43% mathematics, 14% imaging, and 43 % planning). They followed three different
goals including presenting an algorithm for automatically identifying geometries of
pedestrian path segments, tracking pedestrian by high qualification image and identifying
the capacity of urban space to walk. However, we consider those literature related to the
field of pedestrian network planning.
The implementation of pedestrian network was initiated in1797, when French army
occupied Venice, tried to build a proper structure for Venice to link the island clumps by
waterways. They worked to transform the amphibious city into a homogeneous pedestrian
network, through filing canals, building bridges, and creating pathways. Vivo (2016)
believed particularly, walking was a daily habit, a form of networking, and a sign of group
egalitarianism in Sixteenth-Century Venice (de Vivo, 2016). The most recent projects
about the pedestrian network were done in Hong Kong and Toronto. The grade-separated
pedestrian network emerged in the 1970s to weave through diverse outdoor and indoor
pedestrian spaces in a continuous movement experience that creates a collective urban
identity in Hong Kong (Tan & Q.L. Xue, 2014; Tan & Xue, 2015). Toronto’s pedestrian
network is a mostly underground pedestrian walkway network in downtown Toronto that
spans more than 30 kilometers of restaurants, shopping, services and entertainment that
opened in 1987(Bélanger, 2007).
Despite many options for the pedestrian network now available to the planner, there does
not remain a substantial research agenda in terms of both technical and practical
application issues (Kelly et al., 2011). All of the aforementioned works on pedestrian
network implementation were done in processes involving several stages with many
components and their complex relations, which resulted to new structures of pedestrian
network. To analyze their final structures, it is necessary to simplify their processes and the
involved components. Hence, one of the simplified methods is 3W1H analysis (KT
method) or fishbone method with 3 screening steps analysis. The research applies 3W1H
method to study the interferer factors in the pedestrian network model assessment.
2. METHODOLOGY
Hall & Ram (2018), as a pioneer researcher for simplification of the pedestrian network
model, provided the novel analyses using Walk Score. However, he only focused on
individual and independent variables of walking and not the correlation of such variables
in the urban planning context. The primary result shows no review has yet been conducted
specifically on the features of spatial models to assess Pedestrian Network (PN). Thus, the
goal of this study is to review the current pedestrian network models and simplify their
processes and components, in the urban planning framework by 3W1H method. 3W1H
method or Kepner-Tregoe (KT), a rational thinking process, is one of the most unique,
documented analysis and decision-making methods (Chi, Lin, & Liu, 2008). The routing
processing procedure by 3W1H method that is routed object information (What), routed
place information (Where), routing condition information (How) and finally, obtain an
assessing model. To construct the assessing model, the first important step is to completely
and accurately extract knowledge as a set of concepts and relations, and their domain
(Johannessen, Flak, & Sæbø, 2012).
Therefore, according to the urban planning procedure and based on a systematic review of
the academic literature found in the Scopus and Web of Science databases, this paper
intends to answer the following questions: (1) Where is the pedestrian network?; (2) What
are the parameters involved in pedestrian network?, and (3) How to analysis such
parameters?. Figure 1 shows the correlation of these questions based on 3W1H method.
The purpose of the current work is to assess the theoretical and practical issues on the
pedestrian network, in order to elaborate further pedestrian network model.
Fig. 1 Correlation among questions of current paper
3. WHERE IS THE PEDESTRIAN NETWORK?
The question of “Where is the pedestrian network?” identifies the model setting, including
the purpose, scale and user of the model. Pedestrian network setting relates to the practical
context in which the work is carried out.
The aims of PN models are different and usually based on the area that the PN is going to
be formed. The main purposes of these models are: enhancing the potentials for the
interaction of local people in the neighborhood; increasing the urban vitality in downtown;
finding the potential urban space for the pedestrian network in downtown; and to bring
more walk-in shop visiting and purchasing opportunities in central business districts
(CBD) (He et al., 2016; Jabbari, Fonseca, & Ramos, 2017; Lunecke & Mora, 2018; Tal &
Handy, 2012; Tan & Q.L. Xue, 2014). Identifying the capacity of urban space to walk is a
significant focus and main goal for assessment of PN (Hall & Ram, 2018).
In the current models, the architectural body in the pedestrian network is extended to
different-scales and it is necessary to define a specified plan that considers all aspects of
the pedestrian network, such as urban space, street network, and urban structure. Some
researches consider to Micro-scale, Macro-scale, or even Multi-scale (He et al., 2016;
Jabbari et al., 2017; Lunecke & Mora, 2018; Tal & Handy, 2012; Tan & Q.L. Xue, 2014).
The multiscale PN models are based on spatial hierarchical theory (Jabbari et al., 2017;
Lunecke & Mora, 2018). This framework starts with a conceptual model containing
integrated data set, which aims at linking planning hierarchy, analysis hierarchy, and data
hierarchy (Cheng & Masser, 2003). Even though, there are some pedestrian network
models (He et al., 2016; Tal & Handy, 2012; Tan & Q.L. Xue, 2014) that are currently
used in the planning processes, they are often based on narrow data.
Where?
Models Setting
Purpose
Scale
User
What?
Models Content
Criteria
Newtork Structural
Parameters
How?
Models analysis
Approach
Method
Software
Assessment of Pedestrian
Network Model
PN including multifunctional spaces, is involved by different users with often conflicting
interests and PN models reflect aspects of the users (Herrmann, 2016). Elderly and
disabilities people are users who should not be forgotten on PN planning in the public
space of the city (Wijayanti & Pandelaki, 2012). So, some researches study on pedestrian
flow and pedestrian behavior, and some other ones apply survey approach in order to get
reflections of pedestrian aspect. However, it seems that more works are needed on
classifying users and combining results from the user feedback in PN model.
4. WHAT ARE THE PARAMETERS INVOLVED IN PEDESTRIAN NETWORK?
PN models content is a large issue since it not only refers to physical environmental
aspects, but also considers some street network analysis in macro-scale (He et al., 2016;
Jabbari et al., 2017; Lunecke & Mora, 2018; Tal & Handy, 2012; Tan & Q.L. Xue, 2014).
In order to qualify and evaluate those different dimensions, the best way is to classify
contents of existing resources. This classification includes two main contexts. Firstly, some
PN models identified some criteria related to the features of each street suitable for
walking (He et al., 2016; Jabbari et al., 2017; Lunecke & Mora, 2018; Tal & Handy, 2012;
Tan & Q.L. Xue, 2014). Secondly, some other PN models evaluated the street position in
the street network, through combining structural parameters of the pedestrian network such
as connectivity, integration, and distribution (He et al., 2016; Jabbari et al., 2017; Tal &
Handy, 2012)
The studies on criteria related to the features of streets show that such parameters for
walkable streets are highly complex. Understanding what a pedestrian may consider as an
attractive route, can allow planners to build more walkable and livable cities. Physical
environment as the main parameter considering in urban design, is classified into four
groups: built environment, urban function, accessibility and natural environment.
Built environment comprises several perceptual qualities that may affect the walking
environment (Bahrainy & Khosravi, 2013; Garcia & Lara, 2015; Kim, Park, & Lee, 2014;
Wey & Chiu, 2013). Researchers proposed six sub-criteria for this context: imageability,
enclosure, human scale, transparency, complexity and terrain slope (Ewing & Handy,
2009; Lundberg & Weber, 2014). These criteria have been used to create urban design
quality indexes, to capture aspects of the built environment related to people’s emotive
responses to aesthetics in urban areas (Ferrer, Ruiz, & Mars, 2015; Jabbari et al., 2017;
Lunecke & Mora, 2018).
Urban function impacts on the space activity. Land Use as one of urban function sub-
criterion influences the satisfaction and distribution of pedestrians in urban spaces
(Bahrainy & Khosravi, 2013; Lamíquiz & López-Domínguez, 2015; Lerman & Omer,
2016). Population density is another sub-criterion, which is mostly used in this topic
(Jabbari et al., 2017; Tal & Handy, 2012). Particularly, population density is a correlation
between residential areas and pedestrians’ movements (Lerman & Omer, 2016; Peiravian,
et al., 2014).
Accessibility is other criterion analyzed by several authors that may enhance the speedily
access of pedestrian to a certain place. It includes public transportation services and
intelligent transportation system (ITS) as a sustainable method of urban mobility (Grecu &
Morar, 2013). Accessibility is a facility which strongly links the urban function to the built
environment. For instance, in order to join the suburban areas that are mostly car-
dependent to the PN, accessibility can enhance the walking through transportation
purposes (Gilderbloom et al., 2015; Lamíquiz & López-Domínguez, 2015).
Natural environment of streets and urban areas is also an important criterion, which
influences walking (Panagopoulos, Duque, & Dan, 2016). Comfortable conditions,
including temperature, green space, sunlight, shade and wind are important for walk (Koh
& Wong, 2013). Some authors developed urban green space walkability approaches to
improve walkability in urban areas (Lwin & Murayama, 2011).
Lastly, in order to evaluate street positions in the PN, some structural parameters have been
assessed to a better understanding of the street spatial configuration, street network, the
location of economic activities, and the numerical levels of street life (Gilderbloom, Riggs,
& Meares, 2015; Kim et al., 2014; Lerman & Omer, 2016; Millward et al., 2013; Peiravian
et al., 2014). Connectivity is the primary parameter of any transportation network. It links
locations people want to travel between and impacts on walking and on defining how
streets are networked (Azmi & Ahmad, 2015; Bahrainy & Khosravi, 2013).
Street network integration is another parameter in urban morphology, to analyze pedestrian
movement pattern (Carpio-Pinedo et al., 2014; Koohsari et al., 2016). Furthermore, Tan
(2014) studied the distribution of pedestrian mobility into the street network, as another
parameter. He (2016) believed that the distribution of pedestrian flows identifies these
multi-level pedestrian systems.
5. HOW To ANALYSIS SUCH PARAMETERS?
Urban planning for the PN is the complex job. The decision making of urban planning
needs to consider the physical structure of the city along with economic, social and
environmental factors in the different scale. However, in 33 of the 42 (79%) studies the
Walk Score by Hall & Ram (2018) was used as an independent variable, only once as a
mediating-moderating variable (Brown et al., 2014) and on no occasion as a dependent
variable. In eight papers (18%) the Walk Score was a part of a bivariate correlation model
(Duncan et al., 2016; Hall & Ram, 2018; Towne et al., 2016). Therefore, the urban
information model about PN should integrate the multidimensional urban aspects of
economy, society, and environment. The link among different criteria introduced by this
paper, did through different models in order to study the relationships between the
potential of urban space and pedestrian.
In the planning literature, considerable research has focused on relationships between
walking and all those criteria. These factors are usually composed by several criteria and
sub-criteria that are co-related but weighted in different ways (Millward et al., 2013).
Multi-Criteria Analysis (MCA) approach was used in the PN model to address the
complexity of urban mobility issues reflected in the wide set of sustainability indicators
(Jabbari et al., 2017). The MCA enabled through the structured prioritization of a set of
nested variables of urban space related to pedestrian. These approaches were inspired in
the study carried out by Frank (2005) that built a combined walkability index of three
urban criteria to analyze their influence in physical activity (Frank, Schmid, Sallis,
Chapman, & Saelens, 2005). The MCA is also a common used tool namely in spatial
planning. The MCA evaluates decision problems and different options based on specific
criteria or decision maker preferences, by using a number of qualitative and/or quantitative
criteria with different weights (Durmuş & Turk, 2014).
The increasing availability of spatial data with greater disaggregation encouraged the
utilization of Geographic Information System (GIS) in PN model. GIS have been used by
many authors in several tasks such as identifying high and low walkability, providing
information on the walkability characteristics of a given region and generating a
standardized benchmark to compare different settings in terms of characteristics shown to
create the PN (Badland et al., 2013; Kim et al., 2014; Tal & Handy, 2012). In fact many
PN attributes, namely density, land-use mix, street network, and accessibility can be
analyzed in GIS (Guo & Loo, 2013). For those authors, combining GIS data and an
environment audit has proven to produce a valid instrument for assessing the PN. GIS have
been used in spatial analysis namely: to assess the streets network connectivity for bicycle
and pedestrian (Lundberg & Weber, 2014). The GIS techniques are frequently used in
combination with other approaches, namely with: agent-based simulations, where GIS
provide geographical data to modeling neighborhood walkability (Badland et al., 2013).
Pedestrian network requires the full consideration of the spatial continuity of the city
(Yücel, 1979). Streets network connectivity is seen has having important impact in walking
and in defining how the streets are networked (Azmi & Ahmad, 2015; Bahrainy &
Khosravi, 2013). Thus, a convenient design of PN should be provided to encourage
walking and to kept obstacles to a minimum. Streets network connectivity can be defined
as the number of intersecting streets per land-area unit (Azmi & Ahmad, 2015; Garcia &
Lara, 2015). Space syntax has been used to assess streets network connectivity because has
several advantages over more simple streets network connectivity measures like passive
graphic notions. By using axial lines, space syntax is more suitable to calculate movements
in network-configured human settlements and functional connectivity in networks
(Gilderbloom et al., 2015; Jabbari et al., 2017; Kim et al., 2014; Lerman & Omer, 2016;
Millward et al., 2013; Peiravian et al., 2014; Tianxiang, Dong, & Shoubing, 2015).
In turn, urban configuration is the primary generator of pedestrian movement patterns.
Peponis et al. (1989) presented some findings about morphology of Greek towns and their
patterns of pedestrian movement. This study compared the pattern of pedestrian movement
and the urban configuration by the typological model of urban layouts. Various measures
of urban configuration are correlated with aspects of social life. Accessibility is based on
the relationships that each space has with the others in an urban system (Jeong & Banyn
2016). Consequently, the use of integration analysis in urban studies raised and developed
PN model in the last years (He et al., 2016). For instance, Li (2016) measured the spatial
configuration of street networks in the Chinese city of Gulangyu through integration
analysis with the purpose of guide planning and tourism management policies and tourist
preferences. Cutini (2016) also used space syntax in order analyze the relationship between
movement and the urban structure of Florence, in order to investigate how the movement
pattern has changed over time as a result of the growth of the conurbation and according to
the progressive transformation of its grid. In this sense, this method is likely to be useful
for further comparison with another model in supporting PN because it deals with both
spatial and functional aspects of urban form.
An attempt is made to classify urban spaces in order to suggest a new typology of public
space, one based on how public space is managed named urban space typology (Carmona,
2010). This approach was applied in the downtown and based on the characteristic of
public space and PN classifies to pedestrian zones. By these pedestrian zones provided PN
brief design document to manage the PN based on the specific feature in future (Tan &
Xue, 2015). In the 1960s pedestrian zones arose in Europe, mainly in the city centers, and
quickly expanded. For example, in 1966 in Germany there were only 63 pedestrian zones,
but in 1972 there were 182 and in 1977 already 370 pedestrian zones (Kostof, 2004;
Lunecke & Mora, 2018). Lunecke & Mora (2018) showed the high volume of pedestrian
flow in street network occurred at certain segments of streets. Those areas usually are in
the proximity to the pedestrian zones that identified based on urban space typology
approach.
6. CONCLUDING
Key questions of PN (Pedestrian Network) by 3W1H method in this paper are (1) Where is
the pedestrian network? (2) What are the parameters involved in pedestrian network?, and
(3) How to analysis such parameters?. Figure 1 shows the correlation of these questions.
This figure links these questions in terms of a framework of issues to consider when
developing and using PN model. Aspects of the setting will influence matters of
walkability content, but these latter two can also interact with each other in how analysis
model.
The paper reviewed new techniques and features used to create the PN assessment models
based on specific database. In fact, these models adapted different types of data with
different goals. The existence data has not been produced on the same scale. In short, it
could be implied that the identification and assessment of a pedestrian network is a
challenging process, mainly in large areas with multi-functionality and different urban and
natural characteristics as shown Table 1.
Table 1 Models evaluation for pedestrian network
Area CBD Neighborhood Downtown
Goal To bring more
walk-in shop
visiting and
purchasing
opportunities
To bring more
walk-in shop
visiting and
purchasing
opportunities
To enhance the
potentials for the
interaction of
local people
To increase
urban vitality
To find the
potential urban
space for
pedestrian
network
Scale Micro-scale Macro-scale Macro-scale Multi-scale Multi-scale
User Citizen/Tourist Citizen/Tourist Local People Citizen/Tourist Citizen/Tourist
Model
Content
Static built
environment data,
dynamic
environmental
behavior data and
Street network
Standard,
Guideline &
Design code
Land use and
Street network
public space
typologies,
pedestrian
flows and retail
uses
Static built
environment
data and Street
network
Method Survey maps, GIS
and Space Syntax
software
Processed to
design pedestrian
network zone and
regulation
GIS Survey and
Typology
Survey, GIS,
Space Syntax
software
Authors/
Year
He et al, 2016 Tan et al, 2014
Tal & Handy,
2012
Lunecke et
al,2018
Jabbari et al,
2017
Strength /
Weaknesse(
Model)
Considering three
main dimensions
sociology,
economic and
urban planning/
Limited study in
Micro-Scale
Creating standard
document related
to PN
Exploring the
effect of the
pedestrian
network on
pedestrian
accessibility and
connectivity/
Limited to GIS
street networks
Connecting the
pedestrian
network to
three-scale in
order to develop
the well-
function
Considering
Multi-scale
model/ Limited
urban planning
There are some aspects that should be notified for the future developments. A combined
system involves not only a group of experts, but also the residents’ opinions that may be
useful to strengthen the robustness of the approach. The physical environment is translated
by the built characteristics of the space that contribute to an overall perception of
walkability. For these reasons, many pedestrian studies found in literature are related with
behavioral aspects associated with the physical environment (Bahrainy & Khosravi, 2013;
Forsyth, Michael Oakes, Lee, & Schmitz, 2009; Gilderbloom et al., 2015; Lamíquiz &
López-Domínguez, 2015; Nasir, Lim, Nahavandi, & Creighton, 2014). It is important to
verify the model output with the pedestrian behavior in reality. This leads to prioritization
of the processing stages and a simple use of PN assessment models for the future
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